Is there a simple way of classifying images of size differing from the input of existing image classifiers?
My guess:
If you add a few CNN layers before the input of the given model and train only those layers while keeping the given model's parameters frozen, you might get better result.
Essentially these few extra layers would &quot;transform&quot; your input image into the appropriate shape, but with more accuracy since its trained and not hard coded.